Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 23:59:54.943852
Analysis finished2021-02-13 00:12:25.810522
Duration12 minutes and 30.87 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01478455009
Minimum-3.376443106
Maximum3.302392471
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:12:33.841312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.376443106
5-th percentile-1.659928602
Q1-0.6417063817
median-0.02837056001
Q30.6405595405
95-th percentile1.652831665
Maximum3.302392471
Range6.678835577
Interquartile range (IQR)1.282265922

Descriptive statistics

Standard deviation0.9893086471
Coefficient of variation (CV)-66.9150323
Kurtosis0.04113907441
Mean-0.01478455009
Median Absolute Deviation (MAD)0.6378575796
Skewness0.02369476202
Sum-14.78455009
Variance0.9787315992
MonotocityNot monotonic
2021-02-12T19:12:42.141632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.051368766481
 
0.1%
-0.22427910621
 
0.1%
2.099405131
 
0.1%
0.4128333551
 
0.1%
-1.9191470511
 
0.1%
2.1357775821
 
0.1%
-0.28932529151
 
0.1%
0.68567304561
 
0.1%
-1.3478072941
 
0.1%
0.046728741241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.3764431061
0.1%
-3.0372910381
0.1%
-2.8432483481
0.1%
-2.6016460111
0.1%
-2.495140021
0.1%
-2.4511892771
0.1%
-2.4027482731
0.1%
-2.3952356661
0.1%
-2.3494257431
0.1%
-2.309003281
0.1%
ValueCountFrequency (%)
3.3023924711
0.1%
2.8548717331
0.1%
2.6266312571
0.1%
2.6098942981
0.1%
2.4863797181
0.1%
2.4769953941
0.1%
2.3908837741
0.1%
2.3792747641
0.1%
2.3789940661
0.1%
2.3362566891
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01921213945
Minimum-2.916405482
Maximum4.611884702
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:12:50.237766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.916405482
5-th percentile-1.493233298
Q1-0.6320608595
median0.02069561533
Q30.6209227658
95-th percentile1.557967488
Maximum4.611884702
Range7.528290183
Interquartile range (IQR)1.252983625

Descriptive statistics

Standard deviation0.9410054173
Coefficient of variation (CV)48.97973074
Kurtosis0.2951706618
Mean0.01921213945
Median Absolute Deviation (MAD)0.6325127108
Skewness0.1426403708
Sum19.21213945
Variance0.8854911953
MonotocityNot monotonic
2021-02-12T19:12:58.641990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.54448063571
 
0.1%
-1.0607365821
 
0.1%
0.82777571381
 
0.1%
0.67727716691
 
0.1%
-0.80593302471
 
0.1%
1.2348784181
 
0.1%
-0.7766570331
 
0.1%
-1.5704202211
 
0.1%
-0.29809684861
 
0.1%
-0.10336354131
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.9164054821
0.1%
-2.5489696551
0.1%
-2.4140535051
0.1%
-2.3809014141
0.1%
-2.2948042381
0.1%
-2.2351946161
0.1%
-2.0927713551
0.1%
-2.0885509141
0.1%
-2.0880150251
0.1%
-2.0583286561
0.1%
ValueCountFrequency (%)
4.6118847021
0.1%
3.0274602561
0.1%
2.6667510611
0.1%
2.6248912411
0.1%
2.5781114171
0.1%
2.5668358611
0.1%
2.5315346721
0.1%
2.5202914361
0.1%
2.4207130491
0.1%
2.2504402881
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01685346103
Minimum-4.041539819
Maximum3.072365448
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:13:06.857897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.041539819
5-th percentile-1.612668562
Q1-0.6955474544
median0.03473988829
Q30.7352175096
95-th percentile1.726854409
Maximum3.072365448
Range7.113905267
Interquartile range (IQR)1.430764964

Descriptive statistics

Standard deviation1.034683037
Coefficient of variation (CV)61.39291121
Kurtosis0.06447931785
Mean0.01685346103
Median Absolute Deviation (MAD)0.7156674089
Skewness-0.07711370747
Sum16.85346103
Variance1.070568986
MonotocityNot monotonic
2021-02-12T19:13:15.082859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0090920038791
 
0.1%
1.4755819631
 
0.1%
0.018445480791
 
0.1%
-0.96741935341
 
0.1%
-0.58321456911
 
0.1%
-0.646921421
 
0.1%
-0.38180511231
 
0.1%
0.72690028491
 
0.1%
1.3924867861
 
0.1%
2.9304107781
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-4.0415398191
0.1%
-3.49589641
0.1%
-2.9809313081
0.1%
-2.7534651011
0.1%
-2.7273724081
0.1%
-2.7203606091
0.1%
-2.681530461
0.1%
-2.6514357211
0.1%
-2.5934986151
0.1%
-2.4725100071
0.1%
ValueCountFrequency (%)
3.0723654481
0.1%
2.9304107781
0.1%
2.8743636081
0.1%
2.733043661
0.1%
2.5350540351
0.1%
2.5219866721
0.1%
2.4367363171
0.1%
2.4273509681
0.1%
2.3988498451
0.1%
2.3704602521
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02281267566
Minimum-3.258875853
Maximum3.097303921
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:13:23.491590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.258875853
5-th percentile-1.728155372
Q1-0.6663782292
median0.0742246108
Q30.7322322899
95-th percentile1.632293063
Maximum3.097303921
Range6.356179774
Interquartile range (IQR)1.398610519

Descriptive statistics

Standard deviation1.033905535
Coefficient of variation (CV)45.32153747
Kurtosis-0.03343725243
Mean0.02281267566
Median Absolute Deviation (MAD)0.7076998716
Skewness-0.0891381421
Sum22.81267566
Variance1.068960655
MonotocityNot monotonic
2021-02-12T19:13:31.916420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.34356254111
 
0.1%
-0.33419296131
 
0.1%
-0.97003136031
 
0.1%
-0.38031465921
 
0.1%
1.1380319241
 
0.1%
-1.3714858731
 
0.1%
-0.64746126831
 
0.1%
-0.033704833341
 
0.1%
0.59361440121
 
0.1%
-1.4476481371
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.2588758531
0.1%
-3.0330341361
0.1%
-2.9654094921
0.1%
-2.9060828391
0.1%
-2.905442881
0.1%
-2.8248114871
0.1%
-2.6539931711
0.1%
-2.6355241331
0.1%
-2.4306035321
0.1%
-2.4119806181
0.1%
ValueCountFrequency (%)
3.0973039211
0.1%
3.0908898481
0.1%
2.9626963811
0.1%
2.9180575591
0.1%
2.7393771591
0.1%
2.6165128951
0.1%
2.6033077331
0.1%
2.5345266411
0.1%
2.4381489161
0.1%
2.3750527781
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01609040813
Minimum-3.018393038
Maximum2.947144857
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:13:41.260919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.018393038
5-th percentile-1.681068141
Q1-0.691713426
median0.002950518283
Q30.7156868621
95-th percentile1.629455483
Maximum2.947144857
Range5.965537894
Interquartile range (IQR)1.407400288

Descriptive statistics

Standard deviation1.008010671
Coefficient of variation (CV)62.64668138
Kurtosis-0.1937272108
Mean0.01609040813
Median Absolute Deviation (MAD)0.7001185924
Skewness0.05795719133
Sum16.09040813
Variance1.016085513
MonotocityNot monotonic
2021-02-12T19:13:49.492694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.218633821
 
0.1%
-0.011554325361
 
0.1%
0.46493273721
 
0.1%
-0.81668561981
 
0.1%
0.095594195981
 
0.1%
-0.49515724971
 
0.1%
0.65424982731
 
0.1%
-0.50262201941
 
0.1%
-2.317993281
 
0.1%
-0.47650356021
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0183930381
0.1%
-2.6903718181
0.1%
-2.6166544661
0.1%
-2.4751635811
0.1%
-2.4573801411
0.1%
-2.317993281
0.1%
-2.2138789451
0.1%
-2.2027399341
0.1%
-2.1768893271
0.1%
-2.1723834521
0.1%
ValueCountFrequency (%)
2.9471448571
0.1%
2.8845765441
0.1%
2.7687406311
0.1%
2.7375694891
0.1%
2.5774038161
0.1%
2.5755734651
0.1%
2.5142770651
0.1%
2.5096328791
0.1%
2.4845021411
0.1%
2.4741880231
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.06924887497
Minimum-3.309190753
Maximum3.069130885
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:13:57.823726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.309190753
5-th percentile-1.622659903
Q1-0.7287572796
median-0.06492137438
Q30.5673113345
95-th percentile1.512885826
Maximum3.069130885
Range6.378321638
Interquartile range (IQR)1.296068614

Descriptive statistics

Standard deviation0.9471817779
Coefficient of variation (CV)-13.67793742
Kurtosis-0.07919887013
Mean-0.06924887497
Median Absolute Deviation (MAD)0.6507924672
Skewness0.007120874143
Sum-69.24887497
Variance0.8971533204
MonotocityNot monotonic
2021-02-12T19:14:06.178331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.089603243871
 
0.1%
1.6523978521
 
0.1%
0.2497339281
 
0.1%
0.64868877231
 
0.1%
-0.8085604151
 
0.1%
0.20672049771
 
0.1%
-0.064020816881
 
0.1%
-0.71958219631
 
0.1%
0.044232408631
 
0.1%
1.0897243251
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.3091907531
0.1%
-2.997388161
0.1%
-2.903748981
0.1%
-2.7431505781
0.1%
-2.6213716681
0.1%
-2.4286349471
0.1%
-2.3148346091
0.1%
-2.2341346041
0.1%
-2.1616988041
0.1%
-2.1574123321
0.1%
ValueCountFrequency (%)
3.0691308851
0.1%
2.7136265651
0.1%
2.5019151831
0.1%
2.3454263291
0.1%
2.3256620581
0.1%
2.2144591891
0.1%
2.1634352281
0.1%
2.148174861
0.1%
2.0761404211
0.1%
2.0150267971
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03558596366
Minimum-3.476858222
Maximum2.872210759
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:14:14.523874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.476858222
5-th percentile-1.484681976
Q1-0.5664160187
median0.05793634204
Q30.6412984388
95-th percentile1.551661878
Maximum2.872210759
Range6.349068981
Interquartile range (IQR)1.207714458

Descriptive statistics

Standard deviation0.9205807223
Coefficient of variation (CV)25.86920875
Kurtosis0.1243127468
Mean0.03558596366
Median Absolute Deviation (MAD)0.611067231
Skewness-0.1002502659
Sum35.58596366
Variance0.8474688663
MonotocityNot monotonic
2021-02-12T19:14:23.146667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.19911217381
 
0.1%
0.28016233021
 
0.1%
1.3145696461
 
0.1%
0.33704373461
 
0.1%
0.70481793481
 
0.1%
-0.71008571991
 
0.1%
-0.66235137741
 
0.1%
1.9355395211
 
0.1%
0.39561855441
 
0.1%
1.0643584211
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4768582221
0.1%
-2.9373614461
0.1%
-2.9325835121
0.1%
-2.5672209881
0.1%
-2.5119365121
0.1%
-2.4800771241
0.1%
-2.4605503011
0.1%
-2.4443974111
0.1%
-2.2952872431
0.1%
-2.2712536611
0.1%
ValueCountFrequency (%)
2.8722107591
0.1%
2.3401734471
0.1%
2.322678711
0.1%
2.2823397191
0.1%
2.2610787411
0.1%
2.2542099141
0.1%
2.2097789241
0.1%
2.1538458181
0.1%
2.1396232631
0.1%
2.1287480281
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06964668343
Minimum-2.899371453
Maximum3.308334522
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:14:31.644013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.899371453
5-th percentile-1.590609281
Q1-0.523209562
median0.08203559619
Q30.6542093828
95-th percentile1.754741944
Maximum3.308334522
Range6.207705975
Interquartile range (IQR)1.177418945

Descriptive statistics

Standard deviation0.9799226274
Coefficient of variation (CV)14.06991086
Kurtosis0.1324762196
Mean0.06964668343
Median Absolute Deviation (MAD)0.5830340285
Skewness0.03594005093
Sum69.64668343
Variance0.9602483557
MonotocityNot monotonic
2021-02-12T19:14:40.211630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44894419891
 
0.1%
-0.02792697431
 
0.1%
2.601616261
 
0.1%
0.80642185761
 
0.1%
-0.98914920871
 
0.1%
0.66477615461
 
0.1%
0.66958006651
 
0.1%
-0.15527949261
 
0.1%
-0.047755982061
 
0.1%
1.0474132641
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8993714531
0.1%
-2.8642529091
0.1%
-2.5984906071
0.1%
-2.4530083481
0.1%
-2.3807101331
0.1%
-2.3276338321
0.1%
-2.2700223091
0.1%
-2.2355592621
0.1%
-2.198154441
0.1%
-2.1943229951
0.1%
ValueCountFrequency (%)
3.3083345221
0.1%
3.3063819941
0.1%
2.7663074891
0.1%
2.69222961
0.1%
2.601616261
0.1%
2.5608946771
0.1%
2.5597977621
0.1%
2.5278780731
0.1%
2.5053361841
0.1%
2.4631327281
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007370790032
Minimum-2.94757438
Maximum2.996609879
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:14:48.575710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.94757438
5-th percentile-1.618382206
Q1-0.6368944624
median-0.01565756681
Q30.6539921712
95-th percentile1.634587923
Maximum2.996609879
Range5.944184259
Interquartile range (IQR)1.290886634

Descriptive statistics

Standard deviation0.9809928382
Coefficient of variation (CV)133.0919527
Kurtosis0.01278314743
Mean0.007370790032
Median Absolute Deviation (MAD)0.6422516161
Skewness0.02793678279
Sum7.370790032
Variance0.9623469485
MonotocityNot monotonic
2021-02-12T19:14:56.906857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.170195331
 
0.1%
1.3734935281
 
0.1%
0.8023200831
 
0.1%
-0.028586288021
 
0.1%
0.099357719821
 
0.1%
2.1693119211
 
0.1%
1.4483313241
 
0.1%
-0.64966369811
 
0.1%
-0.77866951971
 
0.1%
0.71909965621
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.947574381
0.1%
-2.9030030221
0.1%
-2.7472319131
0.1%
-2.7420786921
0.1%
-2.737988381
0.1%
-2.4844958541
0.1%
-2.4556853231
0.1%
-2.4035829741
0.1%
-2.4024888791
0.1%
-2.3674841481
0.1%
ValueCountFrequency (%)
2.9966098791
0.1%
2.7342374051
0.1%
2.6507869151
0.1%
2.6161944851
0.1%
2.5965898751
0.1%
2.5723519121
0.1%
2.5305891711
0.1%
2.4451672561
0.1%
2.4296947571
0.1%
2.4038513191
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006170171067
Minimum-2.865765991
Maximum3.582445601
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T19:15:05.371673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.865765991
5-th percentile-1.655269868
Q1-0.7361710087
median0.04507033055
Q30.7239814004
95-th percentile1.61175812
Maximum3.582445601
Range6.448211592
Interquartile range (IQR)1.460152409

Descriptive statistics

Standard deviation1.018530219
Coefficient of variation (CV)165.0732546
Kurtosis-0.1965980562
Mean0.006170171067
Median Absolute Deviation (MAD)0.7494133138
Skewness0.008025144932
Sum6.170171067
Variance1.037403807
MonotocityNot monotonic
2021-02-12T19:15:13.553312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.2005790561
 
0.1%
-0.18601588661
 
0.1%
0.38265250641
 
0.1%
1.0238478331
 
0.1%
0.062411655971
 
0.1%
0.4706297971
 
0.1%
2.390682671
 
0.1%
-0.21159776741
 
0.1%
1.6997338241
 
0.1%
1.019563811
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8657659911
0.1%
-2.7579667111
0.1%
-2.6208217871
0.1%
-2.5610227851
0.1%
-2.5435286361
0.1%
-2.4787192111
0.1%
-2.3245235161
0.1%
-2.3173601581
0.1%
-2.2456348941
0.1%
-2.2381599971
0.1%
ValueCountFrequency (%)
3.5824456011
0.1%
3.295361681
0.1%
2.9702311541
0.1%
2.5322937131
0.1%
2.5086985111
0.1%
2.506056971
0.1%
2.4041558311
0.1%
2.3961829481
0.1%
2.390682671
0.1%
2.3728293071
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T19:15:30.461473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T19:15:38.856549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Interactions

2021-02-12T19:00:04.173261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:00:12.203499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:00:20.662141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:00:29.215794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:00:37.332209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:00:45.432276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:00:53.650823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:01.896076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:09.927536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:18.147593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:26.433166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:35.997234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:46.115750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:01:53.984719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:01.659363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:09.551737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:17.264311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:25.012610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:32.747939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:40.656512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:48.562679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:02:56.074904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:03.767301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:11.447058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:19.400243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:27.595523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:35.963951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:44.099213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:52.163148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:03:59.910540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:07.718751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:15.479017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:23.259646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:30.949495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:38.652616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:46.671818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:04:54.419693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:02.099658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:10.364052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:18.275117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:26.071165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:33.969168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:41.903317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:49.810785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:05:58.207621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:06.091886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:13.807608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:22.008380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:29.782766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:37.864734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:46.255860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:06:54.323903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:02.910482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:11.007641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:19.244350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:27.139569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:35.519779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:43.339170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:51.162531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:07:59.047405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:07.080299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:14.935949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:23.082195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:31.071239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:39.231377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:47.514876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:08:55.735035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:04.067771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:12.263316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:20.959750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:30.270824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:39.170892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:47.322970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:09:55.486577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:03.732144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:12.044688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:20.537154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:28.952026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:37.138970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:45.404137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:10:53.495265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:01.683813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:09.904061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:18.014246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:26.355046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:34.574485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:43.091400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:51.301207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:11:59.571172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T19:12:07.867144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T19:15:47.252249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T19:15:55.423777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T19:16:03.784191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T19:16:12.293906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T19:12:16.301689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T19:12:25.408466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
01.028100-1.992137-0.2542890.4873060.0912351.083912-1.046137-0.1969711.7314230.7274221
10.2506400.402466-0.557325-0.250796-0.265171-0.5789210.5713000.719801-0.2873440.0631511
2-0.159392-0.158765-0.711327-0.8946822.372195-0.284268-0.118427-0.409017-0.0493970.6080541
30.7711610.558966-0.1240220.6255170.0964640.212691-2.5119370.4973410.487945-1.6362841
40.639496-0.526567-1.090361-2.304374-0.368019-0.8069630.2332630.3545411.127811-0.2265630
5-0.351053-0.9349951.4088060.215245-0.1631390.2824910.283045-0.910362-1.334853-0.2866301
60.283811-0.2454460.789558-0.6633160.8022511.2300170.3370441.275992-0.492083-1.8579150
7-0.692425-1.784079-0.4648410.071771-0.7594700.892033-1.073160-0.862433-1.364871-0.1412090
81.598259-0.014098-0.2702190.7478481.601156-1.114308-0.0581160.4789110.3780440.4917801
9-0.0711950.0957790.117789-1.287886-0.3345980.141755-0.266931-1.126184-0.1313231.3728750

Last rows

X0X1X2X3X4X5X6X7X8X9target
990-0.3315220.420410-0.910854-0.148829-0.173143-0.9315822.209779-0.039708-0.810690-0.2963431
991-0.9018890.5277170.935284-0.2758160.074905-1.0181020.3726432.141695-1.220169-2.7579670
992-0.1873601.676172-0.130369-1.639558-1.387386-1.6114430.8993291.269481-2.4844960.4086861
993-1.095889-1.1447711.8339530.2909450.0022471.5210280.271064-0.793672-1.247721-0.7142751
9940.7798851.4774370.050804-0.223478-1.478924-1.937121-0.156316-0.555225-2.2820850.9937800
995-0.1076630.803407-0.4120970.056848-1.446449-2.0620781.133191-1.7118700.2570310.4980211
996-0.2655290.0766791.785340-0.4310560.263033-0.5646670.568406-0.473352-0.5694730.4608130
9971.7150230.2522700.4137100.248921-0.410408-0.1678970.6177030.4131552.5965900.0220770
9980.2151241.673176-1.228469-1.7854560.922773-1.9605580.3171680.771615-1.609315-1.2142241
9992.109176-1.3409601.339050-0.338570-0.5324720.3646120.9867890.0718371.216665-1.0997501